Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations40
Missing cells6
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 KiB
Average record size in memory179.3 B

Variable types

Numeric17
Categorical5

Alerts

August is highly overall correlated with CountHigh correlation
Count is highly overall correlated with August and 1 other fieldsHigh correlation
February is highly overall correlated with JulyHigh correlation
Firstname is highly overall correlated with Last,First and 1 other fieldsHigh correlation
January is highly overall correlated with df_mycolumn and 1 other fieldsHigh correlation
July is highly overall correlated with FebruaryHigh correlation
Last,First is highly overall correlated with Firstname and 1 other fieldsHigh correlation
Lastname is highly overall correlated with Firstname and 1 other fieldsHigh correlation
May is highly overall correlated with CountHigh correlation
Price is highly overall correlated with Product NameHigh correlation
Product Name is highly overall correlated with PriceHigh correlation
df_mycolumn is highly overall correlated with January and 1 other fieldsHigh correlation
tax is highly overall correlated with January and 1 other fieldsHigh correlation
March has 1 (2.5%) missing values Missing
April has 1 (2.5%) missing values Missing
August has 1 (2.5%) missing values Missing
Count has 3 (7.5%) missing values Missing
ID has unique values Unique
September has unique values Unique
December has unique values Unique
df_mycolumn has unique values Unique
tax has unique values Unique

Reproduction

Analysis started2025-04-11 12:53:43.607503
Analysis finished2025-04-11 12:54:17.135608
Duration33.53 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Unique 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.675
Minimum3
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:17.238580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6.9
Q116.75
median28
Q339.25
95-th percentile48.05
Maximum50
Range47
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation13.982384
Coefficient of variation (CV)0.50523518
Kurtosis-1.2057244
Mean27.675
Median Absolute Deviation (MAD)11.5
Skewness-0.068536146
Sum1107
Variance195.50705
MonotonicityStrictly increasing
2025-04-11T12:54:17.376495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3 1
 
2.5%
5 1
 
2.5%
7 1
 
2.5%
8 1
 
2.5%
9 1
 
2.5%
10 1
 
2.5%
11 1
 
2.5%
12 1
 
2.5%
14 1
 
2.5%
16 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
3 1
2.5%
5 1
2.5%
7 1
2.5%
8 1
2.5%
9 1
2.5%
10 1
2.5%
11 1
2.5%
12 1
2.5%
14 1
2.5%
16 1
2.5%
ValueCountFrequency (%)
50 1
2.5%
49 1
2.5%
48 1
2.5%
47 1
2.5%
46 1
2.5%
45 1
2.5%
44 1
2.5%
43 1
2.5%
41 1
2.5%
40 1
2.5%

Last,First
Categorical

High correlation 

Distinct11
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
Brennan, Micheal
David, Chloe
Albertson, Kathy
Altman, Zoey
Bittiman, William
Other values (6)
17 

Length

Max length19
Median length16.5
Mean length15.1
Min length11

Characters and Unicode

Total characters604
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAltman, Zoey
2nd rowBrennan, Micheal
3rd rowAltman, Zoey
4th rowDavid, Chloe
5th rowBrennan, Micheal

Common Values

ValueCountFrequency (%)
Brennan, Micheal 5
12.5%
David, Chloe 5
12.5%
Albertson, Kathy 5
12.5%
Altman, Zoey 4
10.0%
Bittiman, William 4
10.0%
Ferguson, Elizabeth 3
7.5%
Allenson, Carol 3
7.5%
Counts, Elizabeth 3
7.5%
Davis, William 3
7.5%
Jameson, Robinson 3
7.5%

Length

2025-04-11T12:54:17.513813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
william 7
 
8.8%
elizabeth 6
 
7.5%
brennan 5
 
6.2%
micheal 5
 
6.2%
chloe 5
 
6.2%
david 5
 
6.2%
albertson 5
 
6.2%
kathy 5
 
6.2%
zoey 4
 
5.0%
altman 4
 
5.0%
Other values (10) 29
36.2%

Most occurring characters

ValueCountFrequency (%)
a 52
 
8.6%
l 50
 
8.3%
n 49
 
8.1%
i 46
 
7.6%
e 41
 
6.8%
, 40
 
6.6%
40
 
6.6%
o 37
 
6.1%
t 31
 
5.1%
s 25
 
4.1%
Other values (24) 193
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 52
 
8.6%
l 50
 
8.3%
n 49
 
8.1%
i 46
 
7.6%
e 41
 
6.8%
, 40
 
6.6%
40
 
6.6%
o 37
 
6.1%
t 31
 
5.1%
s 25
 
4.1%
Other values (24) 193
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 52
 
8.6%
l 50
 
8.3%
n 49
 
8.1%
i 46
 
7.6%
e 41
 
6.8%
, 40
 
6.6%
40
 
6.6%
o 37
 
6.1%
t 31
 
5.1%
s 25
 
4.1%
Other values (24) 193
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 52
 
8.6%
l 50
 
8.3%
n 49
 
8.1%
i 46
 
7.6%
e 41
 
6.8%
, 40
 
6.6%
40
 
6.6%
o 37
 
6.1%
t 31
 
5.1%
s 25
 
4.1%
Other values (24) 193
32.0%

Product Name
Categorical

High correlation 

Distinct5
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
Calculator
Mobile
Camera
Laptop
Computer

Length

Max length10
Median length6
Mean length7.25
Min length6

Characters and Unicode

Total characters290
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComputer
2nd rowCalculator
3rd rowCamera
4th rowComputer
5th rowMobile

Common Values

ValueCountFrequency (%)
Calculator 9
22.5%
Mobile 8
20.0%
Camera 8
20.0%
Laptop 8
20.0%
Computer 7
17.5%

Length

2025-04-11T12:54:17.630914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T12:54:17.729840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
calculator 9
22.5%
mobile 8
20.0%
camera 8
20.0%
laptop 8
20.0%
computer 7
17.5%

Most occurring characters

ValueCountFrequency (%)
a 42
14.5%
o 32
11.0%
l 26
9.0%
C 24
8.3%
t 24
8.3%
r 24
8.3%
e 23
7.9%
p 23
7.9%
u 16
 
5.5%
m 15
 
5.2%
Other values (5) 41
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 42
14.5%
o 32
11.0%
l 26
9.0%
C 24
8.3%
t 24
8.3%
r 24
8.3%
e 23
7.9%
p 23
7.9%
u 16
 
5.5%
m 15
 
5.2%
Other values (5) 41
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 42
14.5%
o 32
11.0%
l 26
9.0%
C 24
8.3%
t 24
8.3%
r 24
8.3%
e 23
7.9%
p 23
7.9%
u 16
 
5.5%
m 15
 
5.2%
Other values (5) 41
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 42
14.5%
o 32
11.0%
l 26
9.0%
C 24
8.3%
t 24
8.3%
r 24
8.3%
e 23
7.9%
p 23
7.9%
u 16
 
5.5%
m 15
 
5.2%
Other values (5) 41
14.1%

Price
Categorical

High correlation 

Distinct4
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
3000
15 
500
1000
2000

Length

Max length4
Median length4
Mean length3.775
Min length3

Characters and Unicode

Total characters151
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000
2nd row500
3rd row3000
4th row3000
5th row1000

Common Values

ValueCountFrequency (%)
3000 15
37.5%
500 9
22.5%
1000 8
20.0%
2000 8
20.0%

Length

2025-04-11T12:54:17.841842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T12:54:17.917443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3000 15
37.5%
500 9
22.5%
1000 8
20.0%
2000 8
20.0%

Most occurring characters

ValueCountFrequency (%)
0 111
73.5%
3 15
 
9.9%
5 9
 
6.0%
1 8
 
5.3%
2 8
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111
73.5%
3 15
 
9.9%
5 9
 
6.0%
1 8
 
5.3%
2 8
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111
73.5%
3 15
 
9.9%
5 9
 
6.0%
1 8
 
5.3%
2 8
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111
73.5%
3 15
 
9.9%
5 9
 
6.0%
1 8
 
5.3%
2 8
 
5.3%

January
Real number (ℝ)

High correlation 

Distinct37
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6752.375
Minimum1700
Maximum16000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:18.029284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1700
5-th percentile2354
Q14894.5
median6459.5
Q38648
95-th percentile9797.65
Maximum16000
Range14300
Interquartile range (IQR)3753.5

Descriptive statistics

Standard deviation2810.8437
Coefficient of variation (CV)0.41627483
Kurtosis1.5937216
Mean6752.375
Median Absolute Deviation (MAD)2133.5
Skewness0.54132587
Sum270095
Variance7900842.4
MonotonicityNot monotonic
2025-04-11T12:54:18.148311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
4300 2
 
5.0%
2354 2
 
5.0%
8563 2
 
5.0%
8900 1
 
2.5%
7800 1
 
2.5%
4200 1
 
2.5%
9300 1
 
2.5%
1700 1
 
2.5%
4290 1
 
2.5%
8000 1
 
2.5%
Other values (27) 27
67.5%
ValueCountFrequency (%)
1700 1
2.5%
2354 2
5.0%
2355 1
2.5%
2578 1
2.5%
4200 1
2.5%
4290 1
2.5%
4300 2
5.0%
4578 1
2.5%
5000 1
2.5%
5300 1
2.5%
ValueCountFrequency (%)
16000 1
2.5%
10000 1
2.5%
9787 1
2.5%
9674 1
2.5%
9500 1
2.5%
9300 1
2.5%
9077 1
2.5%
9050 1
2.5%
8900 1
2.5%
8657 1
2.5%

February
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6550.375
Minimum2319
Maximum15389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:18.270726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2319
5-th percentile3055
Q14847.75
median6572.5
Q37883.75
95-th percentile9914.1
Maximum15389
Range13070
Interquartile range (IQR)3036

Descriptive statistics

Standard deviation2588.1819
Coefficient of variation (CV)0.39511965
Kurtosis2.1068093
Mean6550.375
Median Absolute Deviation (MAD)1483.5
Skewness0.8535907
Sum262015
Variance6698685.6
MonotonicityNot monotonic
2025-04-11T12:54:18.400558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
5479 2
 
5.0%
7534 2
 
5.0%
6020 2
 
5.0%
3500 2
 
5.0%
6281 1
 
2.5%
5490 1
 
2.5%
9639 1
 
2.5%
10600 1
 
2.5%
4391 1
 
2.5%
6391 1
 
2.5%
Other values (26) 26
65.0%
ValueCountFrequency (%)
2319 1
2.5%
2352 1
2.5%
3092 1
2.5%
3435 1
2.5%
3465 1
2.5%
3500 2
5.0%
4020 1
2.5%
4200 1
2.5%
4391 1
2.5%
5000 1
2.5%
ValueCountFrequency (%)
15389 1
2.5%
10600 1
2.5%
9878 1
2.5%
9835 1
2.5%
9639 1
2.5%
9018 1
2.5%
8674 1
2.5%
8533 1
2.5%
8192 1
2.5%
7967 1
2.5%

March
Real number (ℝ)

Missing 

Distinct39
Distinct (%)100.0%
Missing1
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean5493.0769
Minimum1685
Maximum13531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:18.521222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1685
5-th percentile2294.2
Q13385
median4685
Q37205.5
95-th percentile9848.4
Maximum13531
Range11846
Interquartile range (IQR)3820.5

Descriptive statistics

Standard deviation2851.1483
Coefficient of variation (CV)0.51904394
Kurtosis0.065181789
Mean5493.0769
Median Absolute Deviation (MAD)1861
Skewness0.78156072
Sum214230
Variance8129046.4
MonotonicityNot monotonic
2025-04-11T12:54:18.660168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
5389 1
 
2.5%
3623 1
 
2.5%
5430 1
 
2.5%
2347 1
 
2.5%
8732 1
 
2.5%
2937 1
 
2.5%
3513 1
 
2.5%
5413 1
 
2.5%
6515 1
 
2.5%
6546 1
 
2.5%
Other values (29) 29
72.5%
ValueCountFrequency (%)
1685 1
2.5%
1864 1
2.5%
2342 1
2.5%
2344 1
2.5%
2345 1
2.5%
2347 1
2.5%
2477 1
2.5%
2648 1
2.5%
2937 1
2.5%
3257 1
2.5%
ValueCountFrequency (%)
13531 1
2.5%
9879 1
2.5%
9845 1
2.5%
9735 1
2.5%
9676 1
2.5%
9347 1
2.5%
8732 1
2.5%
8616 1
2.5%
8353 1
2.5%
7657 1
2.5%

April
Real number (ℝ)

Missing 

Distinct33
Distinct (%)84.6%
Missing1
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean1557.5641
Minimum1200
Maximum2335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:18.780761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1233.9
Q11352.5
median1468
Q31685.5
95-th percentile1974.5
Maximum2335
Range1135
Interquartile range (IQR)333

Descriptive statistics

Standard deviation269.87793
Coefficient of variation (CV)0.17326923
Kurtosis0.3184953
Mean1557.5641
Median Absolute Deviation (MAD)142
Skewness0.89926202
Sum60745
Variance72834.094
MonotonicityNot monotonic
2025-04-11T12:54:18.893425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1546 3
 
7.5%
1369 2
 
5.0%
1468 2
 
5.0%
1336 2
 
5.0%
1235 2
 
5.0%
1475 1
 
2.5%
1455 1
 
2.5%
1458 1
 
2.5%
1614 1
 
2.5%
1964 1
 
2.5%
Other values (23) 23
57.5%
ValueCountFrequency (%)
1200 1
2.5%
1224 1
2.5%
1235 2
5.0%
1256 1
2.5%
1265 1
2.5%
1326 1
2.5%
1328 1
2.5%
1336 2
5.0%
1369 2
5.0%
1423 1
2.5%
ValueCountFrequency (%)
2335 1
2.5%
1979 1
2.5%
1974 1
2.5%
1968 1
2.5%
1964 1
2.5%
1943 1
2.5%
1895 1
2.5%
1867 1
2.5%
1846 1
2.5%
1725 1
2.5%

May
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7175.95
Minimum2548
Maximum19465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:19.016484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2548
5-th percentile2751.95
Q14261
median6885
Q39678.25
95-th percentile12520.9
Maximum19465
Range16917
Interquartile range (IQR)5417.25

Descriptive statistics

Standard deviation3631.0028
Coefficient of variation (CV)0.50599611
Kurtosis2.05085
Mean7175.95
Median Absolute Deviation (MAD)2829.5
Skewness1.1720157
Sum287038
Variance13184181
MonotonicityNot monotonic
2025-04-11T12:54:19.138136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
3734 2
 
5.0%
9832 1
 
2.5%
9787 1
 
2.5%
7303 1
 
2.5%
8463 1
 
2.5%
10378 1
 
2.5%
8742 1
 
2.5%
14932 1
 
2.5%
2732 1
 
2.5%
4853 1
 
2.5%
Other values (29) 29
72.5%
ValueCountFrequency (%)
2548 1
2.5%
2732 1
2.5%
2753 1
2.5%
3235 1
2.5%
3458 1
2.5%
3465 1
2.5%
3718 1
2.5%
3734 2
5.0%
3973 1
2.5%
4357 1
2.5%
ValueCountFrequency (%)
19465 1
2.5%
14932 1
2.5%
12394 1
2.5%
11394 1
2.5%
11384 1
2.5%
10378 1
2.5%
10347 1
2.5%
9844 1
2.5%
9832 1
2.5%
9787 1
2.5%

June
Real number (ℝ)

Distinct39
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15400.975
Minimum10382
Maximum20473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:19.262432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10382
5-th percentile10901.85
Q112885.5
median15143.5
Q318686.25
95-th percentile19611.4
Maximum20473
Range10091
Interquartile range (IQR)5800.75

Descriptive statistics

Standard deviation3122.2664
Coefficient of variation (CV)0.20273174
Kurtosis-1.4240367
Mean15400.975
Median Absolute Deviation (MAD)2620
Skewness0.040955471
Sum616039
Variance9748547.5
MonotonicityNot monotonic
2025-04-11T12:54:19.386375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
19374 2
 
5.0%
18930 1
 
2.5%
10382 1
 
2.5%
17349 1
 
2.5%
12937 1
 
2.5%
11273 1
 
2.5%
14392 1
 
2.5%
12731 1
 
2.5%
19263 1
 
2.5%
12232 1
 
2.5%
Other values (29) 29
72.5%
ValueCountFrequency (%)
10382 1
2.5%
10500 1
2.5%
10923 1
2.5%
11047 1
2.5%
11273 1
2.5%
12122 1
2.5%
12232 1
2.5%
12320 1
2.5%
12654 1
2.5%
12731 1
2.5%
ValueCountFrequency (%)
20473 1
2.5%
19676 1
2.5%
19608 1
2.5%
19374 2
5.0%
19283 1
2.5%
19263 1
2.5%
19065 1
2.5%
18930 1
2.5%
18774 1
2.5%
18657 1
2.5%

July
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1569.275
Minimum1002
Maximum1983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:19.510882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile1088.5
Q11355.75
median1543
Q31840.25
95-th percentile1977.3
Maximum1983
Range981
Interquartile range (IQR)484.5

Descriptive statistics

Standard deviation276.28812
Coefficient of variation (CV)0.176061
Kurtosis-0.81476686
Mean1569.275
Median Absolute Deviation (MAD)212
Skewness-0.20121902
Sum62771
Variance76335.128
MonotonicityNot monotonic
2025-04-11T12:54:19.641664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1543 2
 
5.0%
1983 2
 
5.0%
1939 2
 
5.0%
1356 2
 
5.0%
1864 2
 
5.0%
1329 1
 
2.5%
1853 1
 
2.5%
1093 1
 
2.5%
1503 1
 
2.5%
1266 1
 
2.5%
Other values (25) 25
62.5%
ValueCountFrequency (%)
1002 1
2.5%
1003 1
2.5%
1093 1
2.5%
1245 1
2.5%
1266 1
2.5%
1283 1
2.5%
1326 1
2.5%
1329 1
2.5%
1345 1
2.5%
1355 1
2.5%
ValueCountFrequency (%)
1983 2
5.0%
1977 1
2.5%
1939 2
5.0%
1876 1
2.5%
1874 1
2.5%
1864 2
5.0%
1853 1
2.5%
1836 1
2.5%
1792 1
2.5%
1753 1
2.5%

August
Real number (ℝ)

High correlation  Missing 

Distinct38
Distinct (%)97.4%
Missing1
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean5503.2308
Minimum2284
Maximum9763
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:19.754876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2284
5-th percentile2543
Q13487
median4653
Q37769.5
95-th percentile9556.7
Maximum9763
Range7479
Interquartile range (IQR)4282.5

Descriptive statistics

Standard deviation2445.2093
Coefficient of variation (CV)0.44432251
Kurtosis-1.3147551
Mean5503.2308
Median Absolute Deviation (MAD)1804
Skewness0.40385239
Sum214626
Variance5979048.4
MonotonicityNot monotonic
2025-04-11T12:54:19.893180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2728 2
 
5.0%
3496 1
 
2.5%
3497 1
 
2.5%
9743 1
 
2.5%
8623 1
 
2.5%
2345 1
 
2.5%
4598 1
 
2.5%
3564 1
 
2.5%
3464 1
 
2.5%
2849 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
2284 1
2.5%
2345 1
2.5%
2565 1
2.5%
2728 2
5.0%
2845 1
2.5%
2849 1
2.5%
3246 1
2.5%
3464 1
2.5%
3478 1
2.5%
3496 1
2.5%
ValueCountFrequency (%)
9763 1
2.5%
9743 1
2.5%
9536 1
2.5%
8992 1
2.5%
8765 1
2.5%
8623 1
2.5%
8567 1
2.5%
8564 1
2.5%
8461 1
2.5%
7883 1
2.5%

September
Real number (ℝ)

Unique 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8102.1
Minimum3456
Maximum17955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:20.016843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3456
5-th percentile3647.75
Q15422
median8208
Q39693.75
95-th percentile14273.4
Maximum17955
Range14499
Interquartile range (IQR)4271.75

Descriptive statistics

Standard deviation3176.8838
Coefficient of variation (CV)0.39210622
Kurtosis1.3890089
Mean8102.1
Median Absolute Deviation (MAD)1647.5
Skewness0.86383424
Sum324084
Variance10092591
MonotonicityNot monotonic
2025-04-11T12:54:20.144599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
4828 1
 
2.5%
8462 1
 
2.5%
8575 1
 
2.5%
8846 1
 
2.5%
5588 1
 
2.5%
10399 1
 
2.5%
15003 1
 
2.5%
7452 1
 
2.5%
4028 1
 
2.5%
8746 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
3456 1
2.5%
3567 1
2.5%
3652 1
2.5%
4028 1
2.5%
4567 1
2.5%
4729 1
2.5%
4754 1
2.5%
4828 1
2.5%
4853 1
2.5%
4924 1
2.5%
ValueCountFrequency (%)
17955 1
2.5%
15003 1
2.5%
14235 1
2.5%
11457 1
2.5%
10789 1
2.5%
10785 1
2.5%
10399 1
2.5%
9876 1
2.5%
9835 1
2.5%
9756 1
2.5%

October
Real number (ℝ)

Distinct39
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6630.05
Minimum1345
Maximum17443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:20.266012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1345
5-th percentile2456.4
Q13329.25
median6362.5
Q38581.25
95-th percentile13579.4
Maximum17443
Range16098
Interquartile range (IQR)5252

Descriptive statistics

Standard deviation3933.9967
Coefficient of variation (CV)0.59335852
Kurtosis0.44021435
Mean6630.05
Median Absolute Deviation (MAD)2951.5
Skewness0.9363721
Sum265202
Variance15476330
MonotonicityNot monotonic
2025-04-11T12:54:20.394236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
3246 2
 
5.0%
12664 1
 
2.5%
6347 1
 
2.5%
2578 1
 
2.5%
9646 1
 
2.5%
5573 1
 
2.5%
8524 1
 
2.5%
3674 1
 
2.5%
5789 1
 
2.5%
17443 1
 
2.5%
Other values (29) 29
72.5%
ValueCountFrequency (%)
1345 1
2.5%
2445 1
2.5%
2457 1
2.5%
2458 1
2.5%
2545 1
2.5%
2578 1
2.5%
2597 1
2.5%
2857 1
2.5%
3246 2
5.0%
3357 1
2.5%
ValueCountFrequency (%)
17443 1
2.5%
15734 1
2.5%
13466 1
2.5%
12876 1
2.5%
12664 1
2.5%
10433 1
2.5%
9646 1
2.5%
9644 1
2.5%
9578 1
2.5%
8753 1
2.5%

November
Real number (ℝ)

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6054.325
Minimum1043
Maximum9957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:20.528268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1043
5-th percentile2318.2
Q13645.75
median5347
Q38668.75
95-th percentile9836.6
Maximum9957
Range8914
Interquartile range (IQR)5023

Descriptive statistics

Standard deviation2753.9586
Coefficient of variation (CV)0.45487458
Kurtosis-1.4716296
Mean6054.325
Median Absolute Deviation (MAD)2627
Skewness-0.048759417
Sum242173
Variance7584287.8
MonotonicityNot monotonic
2025-04-11T12:54:20.663057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
5347 2
 
5.0%
4643 2
 
5.0%
4564 1
 
2.5%
8445 1
 
2.5%
7746 1
 
2.5%
8463 1
 
2.5%
3558 1
 
2.5%
9957 1
 
2.5%
2246 1
 
2.5%
8946 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
1043 1
2.5%
2246 1
2.5%
2322 1
2.5%
2486 1
2.5%
2567 1
2.5%
2675 1
2.5%
2765 1
2.5%
2876 1
2.5%
3357 1
2.5%
3558 1
2.5%
ValueCountFrequency (%)
9957 1
2.5%
9867 1
2.5%
9835 1
2.5%
9684 1
2.5%
9577 1
2.5%
9547 1
2.5%
8946 1
2.5%
8857 1
2.5%
8764 1
2.5%
8746 1
2.5%

December
Real number (ℝ)

Unique 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6671.525
Minimum2324
Maximum15887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:20.788403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2324
5-th percentile2394.45
Q14048
median6760.5
Q38673.75
95-th percentile9910.7
Maximum15887
Range13563
Interquartile range (IQR)4625.75

Descriptive statistics

Standard deviation2907.8995
Coefficient of variation (CV)0.43586729
Kurtosis0.87350876
Mean6671.525
Median Absolute Deviation (MAD)2052
Skewness0.5637456
Sum266861
Variance8455879.5
MonotonicityNot monotonic
2025-04-11T12:54:20.921037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2397 1
 
2.5%
3456 1
 
2.5%
2346 1
 
2.5%
8655 1
 
2.5%
3457 1
 
2.5%
8856 1
 
2.5%
9535 1
 
2.5%
2324 1
 
2.5%
8566 1
 
2.5%
6534 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
2324 1
2.5%
2346 1
2.5%
2397 1
2.5%
3344 1
2.5%
3427 1
2.5%
3456 1
2.5%
3457 1
2.5%
3458 1
2.5%
3465 1
2.5%
3478 1
2.5%
ValueCountFrequency (%)
15887 1
2.5%
10646 1
2.5%
9872 1
2.5%
9674 1
2.5%
9546 1
2.5%
9535 1
2.5%
8856 1
2.5%
8769 1
2.5%
8765 1
2.5%
8676 1
2.5%

df_mycolumn
Real number (ℝ)

High correlation  Unique 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13302.75
Minimum9211
Maximum20689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:21.049963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9211
5-th percentile10009.6
Q110854.5
median13142
Q315032.5
95-th percentile18535.2
Maximum20689
Range11478
Interquartile range (IQR)4178

Descriptive statistics

Standard deviation2867.2119
Coefficient of variation (CV)0.21553527
Kurtosis-0.1970205
Mean13302.75
Median Absolute Deviation (MAD)2265
Skewness0.74419192
Sum532110
Variance8220903.9
MonotonicityNot monotonic
2025-04-11T12:54:21.177092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
14900 1
 
2.5%
18539 1
 
2.5%
13290 1
 
2.5%
15581 1
 
2.5%
11731 1
 
2.5%
10718 1
 
2.5%
10681 1
 
2.5%
13441 1
 
2.5%
20689 1
 
2.5%
14020 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
9211 1
2.5%
9888 1
2.5%
10016 1
2.5%
10289 1
2.5%
10321 1
2.5%
10453 1
2.5%
10560 1
2.5%
10579 1
2.5%
10681 1
2.5%
10718 1
2.5%
ValueCountFrequency (%)
20689 1
2.5%
18539 1
2.5%
18535 1
2.5%
18319 1
2.5%
17530 1
2.5%
17096 1
2.5%
16097 1
2.5%
15635 1
2.5%
15581 1
2.5%
15430 1
2.5%

tax
Real number (ℝ)

High correlation  Unique 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1995.4125
Minimum1381.65
Maximum3103.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:21.296617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1381.65
5-th percentile1501.44
Q11628.175
median1971.3
Q32254.875
95-th percentile2780.28
Maximum3103.35
Range1721.7
Interquartile range (IQR)626.7

Descriptive statistics

Standard deviation430.08178
Coefficient of variation (CV)0.21553527
Kurtosis-0.1970205
Mean1995.4125
Median Absolute Deviation (MAD)339.75
Skewness0.74419192
Sum79816.5
Variance184970.34
MonotonicityNot monotonic
2025-04-11T12:54:21.429566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2235 1
 
2.5%
2780.85 1
 
2.5%
1993.5 1
 
2.5%
2337.15 1
 
2.5%
1759.65 1
 
2.5%
1607.7 1
 
2.5%
1602.15 1
 
2.5%
2016.15 1
 
2.5%
3103.35 1
 
2.5%
2103 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
1381.65 1
2.5%
1483.2 1
2.5%
1502.4 1
2.5%
1543.35 1
2.5%
1548.15 1
2.5%
1567.95 1
2.5%
1584 1
2.5%
1586.85 1
2.5%
1602.15 1
2.5%
1607.7 1
2.5%
ValueCountFrequency (%)
3103.35 1
2.5%
2780.85 1
2.5%
2780.25 1
2.5%
2747.85 1
2.5%
2629.5 1
2.5%
2564.4 1
2.5%
2414.55 1
2.5%
2345.25 1
2.5%
2337.15 1
2.5%
2314.5 1
2.5%

Salary
Real number (ℝ)

Distinct7
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4310
Minimum2000
Maximum7000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:21.529065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q13000
median4250
Q35000
95-th percentile7000
Maximum7000
Range5000
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation1390.4067
Coefficient of variation (CV)0.32260016
Kurtosis-0.36551332
Mean4310
Median Absolute Deviation (MAD)1000
Skewness0.21662996
Sum172400
Variance1933230.8
MonotonicityNot monotonic
2025-04-11T12:54:21.617279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5000 8
20.0%
3000 8
20.0%
4000 8
20.0%
4500 4
10.0%
5600 4
10.0%
2000 4
10.0%
7000 4
10.0%
ValueCountFrequency (%)
2000 4
10.0%
3000 8
20.0%
4000 8
20.0%
4500 4
10.0%
5000 8
20.0%
5600 4
10.0%
7000 4
10.0%
ValueCountFrequency (%)
7000 4
10.0%
5600 4
10.0%
5000 8
20.0%
4500 4
10.0%
4000 8
20.0%
3000 8
20.0%
2000 4
10.0%

Lastname
Categorical

High correlation 

Distinct11
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
Brennan
David
Albertson
Altman
Bittiman
Other values (6)
17 

Length

Max length9
Median length7
Mean length6.875
Min length5

Characters and Unicode

Total characters275
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAltman
2nd rowBrennan
3rd rowAltman
4th rowDavid
5th rowBrennan

Common Values

ValueCountFrequency (%)
Brennan 5
12.5%
David 5
12.5%
Albertson 5
12.5%
Altman 4
10.0%
Bittiman 4
10.0%
Ferguson 3
7.5%
Allenson 3
7.5%
Counts 3
7.5%
Davis 3
7.5%
Jameson 3
7.5%

Length

2025-04-11T12:54:21.734247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brennan 5
12.5%
david 5
12.5%
albertson 5
12.5%
altman 4
10.0%
bittiman 4
10.0%
ferguson 3
7.5%
allenson 3
7.5%
counts 3
7.5%
davis 3
7.5%
jameson 3
7.5%

Most occurring characters

ValueCountFrequency (%)
n 43
15.6%
a 24
 
8.7%
s 22
 
8.0%
e 21
 
7.6%
t 20
 
7.3%
o 19
 
6.9%
l 17
 
6.2%
i 16
 
5.8%
r 15
 
5.5%
A 12
 
4.4%
Other values (11) 66
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 275
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 43
15.6%
a 24
 
8.7%
s 22
 
8.0%
e 21
 
7.6%
t 20
 
7.3%
o 19
 
6.9%
l 17
 
6.2%
i 16
 
5.8%
r 15
 
5.5%
A 12
 
4.4%
Other values (11) 66
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 275
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 43
15.6%
a 24
 
8.7%
s 22
 
8.0%
e 21
 
7.6%
t 20
 
7.3%
o 19
 
6.9%
l 17
 
6.2%
i 16
 
5.8%
r 15
 
5.5%
A 12
 
4.4%
Other values (11) 66
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 275
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 43
15.6%
a 24
 
8.7%
s 22
 
8.0%
e 21
 
7.6%
t 20
 
7.3%
o 19
 
6.9%
l 17
 
6.2%
i 16
 
5.8%
r 15
 
5.5%
A 12
 
4.4%
Other values (11) 66
24.0%

Firstname
Categorical

High correlation 

Distinct9
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
William
Elizabeth
Kathy
Micheal
Chloe
Other values (4)
12 

Length

Max length9
Median length8
Mean length6.225
Min length3

Characters and Unicode

Total characters249
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZoey
2nd rowMicheal
3rd rowZoey
4th rowChloe
5th rowMicheal

Common Values

ValueCountFrequency (%)
William 7
17.5%
Elizabeth 6
15.0%
Kathy 5
12.5%
Micheal 5
12.5%
Chloe 5
12.5%
Zoey 4
10.0%
Carol 3
7.5%
Robinson 3
7.5%
Tia 2
 
5.0%

Length

2025-04-11T12:54:21.849904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T12:54:21.949873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
william 7
17.5%
elizabeth 6
15.0%
kathy 5
12.5%
micheal 5
12.5%
chloe 5
12.5%
zoey 4
10.0%
carol 3
7.5%
robinson 3
7.5%
tia 2
 
5.0%

Most occurring characters

ValueCountFrequency (%)
l 33
13.3%
i 30
12.0%
a 28
11.2%
h 21
 
8.4%
e 20
 
8.0%
o 18
 
7.2%
t 11
 
4.4%
y 9
 
3.6%
b 9
 
3.6%
C 8
 
3.2%
Other values (13) 62
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 33
13.3%
i 30
12.0%
a 28
11.2%
h 21
 
8.4%
e 20
 
8.0%
o 18
 
7.2%
t 11
 
4.4%
y 9
 
3.6%
b 9
 
3.6%
C 8
 
3.2%
Other values (13) 62
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 33
13.3%
i 30
12.0%
a 28
11.2%
h 21
 
8.4%
e 20
 
8.0%
o 18
 
7.2%
t 11
 
4.4%
y 9
 
3.6%
b 9
 
3.6%
C 8
 
3.2%
Other values (13) 62
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 33
13.3%
i 30
12.0%
a 28
11.2%
h 21
 
8.4%
e 20
 
8.0%
o 18
 
7.2%
t 11
 
4.4%
y 9
 
3.6%
b 9
 
3.6%
C 8
 
3.2%
Other values (13) 62
24.9%

Count
Real number (ℝ)

High correlation  Missing 

Distinct37
Distinct (%)100.0%
Missing3
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean57607.892
Minimum39091
Maximum80862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2025-04-11T12:54:22.094801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum39091
5-th percentile46935.4
Q151538
median56641
Q363621
95-th percentile69252.4
Maximum80862
Range41771
Interquartile range (IQR)12083

Descriptive statistics

Standard deviation8786.0136
Coefficient of variation (CV)0.15251406
Kurtosis0.88751511
Mean57607.892
Median Absolute Deviation (MAD)5668
Skewness0.63468389
Sum2131492
Variance77194036
MonotonicityNot monotonic
2025-04-11T12:54:22.212527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
51587 1
 
2.5%
60691 1
 
2.5%
60047 1
 
2.5%
65630 1
 
2.5%
66104 1
 
2.5%
51632 1
 
2.5%
64806 1
 
2.5%
66343 1
 
2.5%
48194 1
 
2.5%
54238 1
 
2.5%
Other values (27) 27
67.5%
(Missing) 3
 
7.5%
ValueCountFrequency (%)
39091 1
2.5%
46921 1
2.5%
46939 1
2.5%
47627 1
2.5%
48194 1
2.5%
48946 1
2.5%
49512 1
2.5%
49768 1
2.5%
50973 1
2.5%
51538 1
2.5%
ValueCountFrequency (%)
80862 1
2.5%
79830 1
2.5%
66608 1
2.5%
66384 1
2.5%
66343 1
2.5%
66104 1
2.5%
65630 1
2.5%
65527 1
2.5%
64806 1
2.5%
63621 1
2.5%

Interactions

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2025-04-11T12:53:47.819006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:50.050352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:51.633663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:53.403277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:54.963655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:56.864516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:58.419018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:00.416544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:02.851918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:04.595433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:06.193991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:08.223134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:09.809660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:11.449259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:13.653168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:15.445412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:45.954672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:47.955078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:50.148828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:51.722125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:53.497382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:55.057224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:56.951322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:58.509774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:00.556308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:02.949927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:04.701116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:06.279412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:08.318030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:09.901115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:11.550926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:13.795092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:15.547822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:46.047902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:48.096482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:50.256587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:51.815893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:53.592309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:55.167243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:57.043495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:58.604551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:00.698291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:03.058519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:04.801606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:06.376768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:08.420206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:09.995800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:11.662216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:13.939750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:15.622890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:46.133132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:48.224362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:50.343163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:51.898400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:53.685774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:55.263201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:57.126764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:58.687596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:00.829371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:03.157730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:04.891956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:06.457802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:08.505751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:10.086634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:11.802871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:14.076158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:15.715319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:46.231738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:48.377733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:50.439723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:51.997667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:53.774095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:55.360266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:57.225034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:58.777193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:00.996260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:03.267744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:04.996364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:06.548406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:08.609098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:10.188190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:11.957797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:14.215261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:15.798338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:46.312025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:48.516941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:50.528967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:52.322253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:53.861557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:55.452895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:57.308668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:58.872355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:01.132554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:03.374116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:05.102608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:06.632441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:08.698731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:10.289719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:12.095181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:14.300367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:15.890823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:46.415301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:48.661377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:50.629539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:52.414798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:53.948882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:55.557991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:57.397103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:58.960377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:01.280825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:03.470318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:05.197629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:06.718137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:08.795516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:10.390935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:12.227023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:14.395760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:15.972442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:46.495701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:48.808918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:50.723554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:52.502898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:54.033894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:55.651013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:57.484311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:53:59.048209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:01.418183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:03.572056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:05.302111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:06.805651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:08.886412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:10.477745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:12.368231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T12:54:14.484438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-11T12:54:22.330945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AprilAugustCountDecemberFebruaryFirstnameIDJanuaryJulyJuneLast,FirstLastnameMarchMayNovemberOctoberPriceProduct NameSalarySeptemberdf_mycolumntax
April1.000-0.088-0.1440.2190.0480.0000.159-0.246-0.077-0.0610.0000.000-0.236-0.2260.0140.0840.0000.0000.2070.071-0.227-0.227
August-0.0881.0000.5690.283-0.0540.0000.1000.317-0.019-0.0210.0000.0000.0730.4290.3450.0230.0670.0000.065-0.2010.3340.334
Count-0.1440.5691.0000.0760.0850.000-0.1110.345-0.0740.4000.0000.0000.2210.6150.061-0.0240.3380.2650.1580.2960.4420.442
December0.2190.2830.0761.000-0.0130.0000.2490.0480.063-0.2030.0000.000-0.3050.0790.225-0.3020.0000.000-0.0430.0670.1080.108
February0.048-0.0540.085-0.0131.0000.000-0.098-0.4600.623-0.0880.0000.000-0.1250.026-0.0180.2510.2440.251-0.155-0.1070.3330.333
Firstname0.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.9670.9670.0000.2100.1470.0000.0000.0000.0000.3320.0000.000
ID0.1590.100-0.1110.249-0.0980.0001.0000.0520.1000.1490.0000.0000.145-0.321-0.092-0.2070.0000.000-0.0020.132-0.171-0.171
January-0.2460.3170.3450.048-0.4600.0000.0521.000-0.4540.0680.0000.0000.0780.1810.076-0.1650.1450.1290.170-0.0910.5920.592
July-0.077-0.019-0.0740.0630.6230.0000.100-0.4541.000-0.0680.0530.053-0.129-0.178-0.0640.3020.2140.351-0.211-0.1080.0370.037
June-0.061-0.0210.400-0.203-0.0880.0000.1490.068-0.0681.0000.0000.0000.117-0.037-0.2110.0990.0000.0000.0920.275-0.135-0.135
Last,First0.0000.0000.0000.0000.0000.9670.0000.0000.0530.0001.0001.0000.0000.2920.0000.0000.2070.0000.1000.3390.0000.000
Lastname0.0000.0000.0000.0000.0000.9670.0000.0000.0530.0001.0001.0000.0000.2920.0000.0000.2070.0000.1000.3390.0000.000
March-0.2360.0730.221-0.305-0.1250.0000.1450.078-0.1290.1170.0000.0001.000-0.087-0.021-0.0230.0000.0000.1030.121-0.036-0.036
May-0.2260.4290.6150.0790.0260.210-0.3210.181-0.178-0.0370.2920.292-0.0871.0000.0260.0950.0000.0000.2690.0240.2830.283
November0.0140.3450.0610.225-0.0180.147-0.0920.076-0.064-0.2110.0000.000-0.0210.0261.000-0.0770.0000.030-0.013-0.2080.1030.103
October0.0840.023-0.024-0.3020.2510.000-0.207-0.1650.3020.0990.0000.000-0.0230.095-0.0771.0000.2320.2220.055-0.3540.0760.076
Price0.0000.0670.3380.0000.2440.0000.0000.1450.2140.0000.2070.2070.0000.0000.0000.2321.0000.9860.0000.2220.1230.123
Product Name0.0000.0000.2650.0000.2510.0000.0000.1290.3510.0000.0000.0000.0000.0000.0300.2220.9861.0000.0000.1520.1700.170
Salary0.2070.0650.158-0.043-0.1550.000-0.0020.170-0.2110.0920.1000.1000.1030.269-0.0130.0550.0000.0001.0000.202-0.024-0.024
September0.071-0.2010.2960.067-0.1070.3320.132-0.091-0.1080.2750.3390.3390.1210.024-0.208-0.3540.2220.1520.2021.000-0.260-0.260
df_mycolumn-0.2270.3340.4420.1080.3330.000-0.1710.5920.037-0.1350.0000.000-0.0360.2830.1030.0760.1230.170-0.024-0.2601.0001.000
tax-0.2270.3340.4420.1080.3330.000-0.1710.5920.037-0.1350.0000.000-0.0360.2830.1030.0760.1230.170-0.024-0.2601.0001.000

Missing values

2025-04-11T12:54:16.685254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-11T12:54:16.885549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-11T12:54:17.051830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDLast,FirstProduct NamePriceJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberdf_mycolumntaxSalaryLastnameFirstnameCount
03Altman, ZoeyComputer30004300106005389.01475.097871038213293497.04828634777462397149002235.005000AltmanZoey51587.0
15Brennan, MichealCalculator500890096393623.01369.098321893018533496.084621266484633456185392780.853000BrennanMicheal66104.0
27Altman, ZoeyCamera3000780054905430.01614.087421734910934598.08575852445642346132901993.504000AltmanZoey60691.0
38David, ChloeComputer3000930062812347.01964.073031293713269743.08846257884458655155812337.154500DavidChloe60047.0
49Brennan, MichealMobile1000420075318732.01646.084631937414738623.05588964699573457117311759.655000BrennanMicheal65630.0
510Albertson, KathyCalculator500170090182937.01643.0103781127319392345.010399557335588856107181607.705600AlbertsonKathy51632.0
611Flores, TiaLaptop2000429063913513.01455.0149321439212663564.015003367489469535106811602.154000FloresTia64806.0
712Bittiman, WilliamCamera3000905043915413.01458.027321273115033464.07452578933572324134412016.153000BittimanWilliam48194.0
814Ferguson, ElizabethMobile10005300153896515.01369.048531926319837643.040281573422468566206893103.352000FergusonElizabeth66343.0
916Allenson, CarolLaptop2000800060206546.01974.037181223214532849.087461744395776534140202103.007000AllensonCarol51538.0
IDLast,FirstProduct NamePriceJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberdf_mycolumntaxSalaryLastnameFirstnameCount
3040David, ChloeCalculator500235475349879.01943.025481390018644593.01145732462675746898881483.205000DavidChloe56072.0
3141Flores, TiaLaptop2000544554793566.01546.045861265415348461.0142352857986710646109241638.603000FloresTia57506.0
3243Jameson, RobinsonComputer3000865798784676.01235.043571865716734653.09835254562455377185352780.254000JamesonRobinson63621.0
3344Bittiman, WilliamMobile1000685434359676.01546.064671967613558564.07954134578956476102891543.354500BittimanWilliam65527.0
3445Altman, ZoeyCalculator500457867999347.0NaN76451387416765347.097561043346434238113771706.555000AltmanZoeyNaN
3546Allenson, CarolLaptop2000967478563578.01846.064561754815437656.03567875398358568175302629.505600AllensonCarol59724.0
3647Albertson, KathyCamera3000235568563748.02335.034581508718743563.07645128765347698792111381.654000AlbertsonKathy46921.0
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